Strategies and issues in the detection of pathway enrichment in genome-wide association studies

Abstract

A fundamental question in human genetics is the degree to which the polygenic character of complex traits derives from polymorphism in genes with similar or with dissimilar functions. The many genome-wide association studies now being performed offer an opportunity to investigate this, and although early attempts are emerging, new tools and modeling strategies still need to be developed and deployed. Towards this goal, we implemented a new algorithm to facilitate the transition from genetic marker lists (principally those generated by PLINK) to pathway analyses of representational gene sets in either threshold or threshold-free downstream applications (e.g. DAVID, GSEA-P, and Ingenuity Pathway Analysis). This was applied to several large genome-wide association studies covering diverse human traits that included type 2 diabetes, Crohn's disease, and plasma lipid levels. Validation of this approach was obtained for plasma HDL levels, where functional categories related to lipid metabolism emerged as the most significant in two independent studies. From analyses of these samples, we highlight and address numerous issues related to this strategy, including appropriate gene based correction statistics, the utility of imputed versus non-imputed marker sets, and the apparent enrichment of pathways due solely to the positional clustering of functionally related genes. The latter in particular emphasizes the importance of studies that directly tie genetic variation to functional characteristics of specific genes. The software freely provided that we have called ProxyGeneLD may resolve an important bottleneck in pathway-based analyses of genome-wide association data. This has allowed us to identify at least one replicable case of pathway enrichment but also to highlight functional gene clustering as a potentially serious problem that may lead to spurious pathway findings if not corrected.

Fig. 1

Change in the ranks of genes in sample 1 (GWAS of plasma HDL levels) following gene based adjustment for the number of markers spanning the longest known splice-form. Adjusted ranks are plotted along the x-axis and unadjusted ranks along the y-axis. The sub-panel shows the running average of the ratio between the unadjusted and adjusted ranks and illustrates that the relative change is large for the top-ranked genes. The appearance of lines in the main panel reflects groups of genes with similar numbers of markers (the steepest being genes represented by single markers, the second steepest by 2 markers, and so on).

Fig. 2

Evidence of wide-spread ontology-based clustering of genes in the human genome. The main panel illustrates the distribution of significance for the first 100 clusters (dark shaded area) against a null distribution for random genes (light shaded area at base). The sub-panel depicts significant clusters according to genome position, ordered from pter of chromosome 1 to qter of chromosome 22. The top three peaks represent “olfactory receptor activity”, “cell adhesion”, and “keratinization”, respectively. See supplementary table 1 for a full listing of significant terms.

Fig. 3

Change in gene ranks for a subset of sample 2 (WTCCC type 2 diabetes) using the top 3% of genes from directly genotyped marker lists (x-axis) compared to the same genes derived from imputed lists (y-axis) (3a). For comparison, in figure 3b the top 3% of genes from imputed lists (x-axis) are compared to directly typed markers (y-axis). In both cases, while some extreme outliers occur, most genes retain similar rank. All axes are drawn in log₁₀ scale.